Precipitation and Greenness in Pastoral Lands of East Turkana, Kenya

Pastoralism has long supported livelihoods and provided essential ecosystem services in landscapes of East Africa. Vegetation productivity is central to the functioning of pastoral systems but may be affected by changes in climate and landuse. Vegetation monitoring is important for understanding the effects of global change in pastoral lands; however, it can be time and resource intensive. Remote sensing provides opportunities for efficient multi-scale monitoring of vegetation and climatic drivers. In this thesis, I explore the utility of satellite and UAV remote sensing for monitoring vegetation and precipitation trends and relationships in the East of Lake Turkana Region of northern Kenya. In Chapter 1, I examine regional greenness and precipitation time series at monthly, seasonal, and annual temporal resolutions, as well as relationships between greenness and precipitation from 2000 to 2022. I found evidence of long-term precipitation–greenness coupling at monthly and annual temporal resolutions. There were no trends in monthly or annual regional precipitation, while NDVI significantly increased at monthly temporal resolution but did not exhibit a significant trend at annual temporal resolution. Traditional pastoral practices, such as use of livestock corrals (bomas), also influence local vegetation composition and abundance. In chapter two, I use satellite and unmanned aerial vehicle (UAV) remote sensing data to monitor greenness in and around abandoned boma settlements at seasonal and annual temporal resolutions. Results showed that mean NDVI from UAV and Sentinel-2 data varied based on seasons (dry or wet) and from boma to boma. NDVI significantly differed between bomas and non-boma sites and there was significant positive correlation between NDVI with precipitation across all bomas, with an optimum temporal lag response of one month. Collectively, my results add to the body of literature demonstrating the utility of satellite and UAV-based remote sensing data for monitoring vegetation in pastoral systems. Advisor: Daniel R. Ude

Mobility classification of cattle with micro-Doppler radar

Lameness in dairy cattle is a welfare concern that negatively impacts animal productivity and farmer profitability. Micro-Doppler radar sensing has been previously suggested as a potential system for automating lameness detection in ruminants. This thesis investigates the refinement of the proposed automated system by analysing and enhancing the repeatability and accuracy of the existing scoring method in cattle mobility scoring, used to provide labels in machine learning. The main aims of the thesis were (1) to quantify the performance of the micro-Doppler radar sensing method for the assessment of mobility, (2) to characterise and validate micro-Doppler radar signatures of dairy cattle with varying degrees of gait impairment, and (3) to develop machine learning algorithms that can infer the mobility status of the animals under test from their radar signatures and support automatic contactless classification. The first study investigated inter-assessor agreement using a 4-level system and modifications to it, as well as the impact of factors such as mobility scoring experience, confidence in scoring decisions, and video characteristics. The results revealed low levels of agreement between assessors' scores, with kappa values ranging from 0.16 to 0.53. However, after transforming and reducing the mobility scoring system levels, an improvement was observed, with kappa values ranging from 0.2 to 0.67. Subsequently, a longitudinal study was conducted using good-agreement scores as ground truth labels in supervised machine-learning models. However, the accuracy of the algorithmic models was found to be insufficient, ranging from 0.57 to 0.63. To address this issue, different labelling systems and data pre-processing techniques were explored in a cross-sectional study. Nonetheless, the inter-assessor agreement remained challenging, with an average kappa value of 0.37 (SD = 0.16), and high-accuracy algorithmic predictions remained elusive, with an average accuracy of 56.1 (SD =16.58). Finally, the algorithms' performance was tested with high-confidence labels, which consisted of only scores 0 and 3 of the AHDB system. This testing resulted in good classification accuracy (0.82), specificity (0.79), and sensitivity (0.85). This led to the proposal of a new approach to producing labels, testing vantage point changes, and improving the performance of machine learning models (average accuracy = 0.7 & SD = 0.17, average sensitivity = 0.68 & SD = 0.27, average specificity = 0.75 & SD = 0.17). The research identified a challenge in creating high-confidence diagnostic labels for supervised machine learning-based algorithms to automate the detection and classification of lameness in dairy cows. As a result, the original goals were partially overridden, with the focus shifted to creating reliable labels that would perform well with radar data and machine learning. This point was considered necessary for smooth system development and process automation. Nevertheless, we managed to quantify the performance of the micro-Doppler radar system, partially develop the supervised machine learning algorithms, compare levels of agreement among multiple assessors, evaluate the assessment tools, assess the mobility evaluation process and gather a valuable data set which can be used as a foundation for subsequent studies. Finally, the thesis suggests changes in the assessment process to improve the prediction accuracy of algorithms based on supervised machine learning with radar data

Temporal changes in Land Use and Land Cover (LULC) and local climate in the Krueng Peusangan Watershed (KPW) area, Aceh, Indonesia

Watersheds are important sources of various ecosystem services. The Krueng Peusangan Watershed (KPW), one of the watersheds in Aceh Province, is expected to become the "lungs" of the ecology of the north-central part of Aceh. Currently, the KPW is one of the watersheds that suffers from severe or critical damage, especially that caused by changes in forest cover. Spatiotemporal monitoring of changes in landscape patterns (composition and configuration) is needed to inform policy and support planning for sustainable watershed management. This study aims to: 1) determine the pattern of changes in LULC in KPW in two decades (1999–2009 and 2009–2019, and 2) determine the impact of changes in the LULC pattern on the local climate. Landsat Satellite Imagery for three years (1999, 2009, and 2019) is used to classify LULC. Geographic Information System (GIS) technology and remote sensing are used to analyze it. Each satellite image is classified into six categories: built-up area, forest, agriculture, bare land, wetland, and water body. This classification resulted in LULC maps for 1999, 2009, and 2019 with kappa coefficients of 0.84, 0.88, and 0.84. It is found that between 1999–2009 and 2009–2019, there has been a consistent decrease in forest cover area and an increase in built-up area. Local climate change is also occurring in this KPW. Continuous monitoring of LULC changes in KPW is also necessary to keep management planning up to date

Modeling Impact of Changing Hydroclimatic Regime on Dissolved Organic Carbon Export from Baker Creek Catchment

The subarctic is anticipated to undergo hydroclimatic regime change, which can impact hydrological processes and water yield. Explaining landscape-scale carbon (C) budgets and pollutant transfer is necessary for understanding the impact of changing hydroclimatic regimes. This research investigates dissolved organic carbon (DOC) fluxes in a hydrologically complex watershed (Baker Creek) in the Northwest Territories. Discharge, DOC concentration, and DOC export were simulated using a rainfall-runoff model (PERSiST), and a catchment biogeochemical model (INCA-C). Model calibration (2012–2016) was done using available discharge and DOC concentration data in sub-catchments of Baker Creek. The model successfully reproduced hydrological flow in the catchment (R2: 0.87–0.94; NS: 0.82–0.91) and reasonably captured DOC concentration (R2: 0.19–0.31). Future conditions were simulated using two climate scenarios (elevated temperature, elevated temperature and precipitation), and compared against a scenario with baseline conditions. Average discharge over 30 years is predicted to decrease under elevated temperature scenario (22–27% of baseline) and increase (116–175% of baseline) under elevated temperature and precipitation scenario. For this scenario, discharge increases in early winter indicate a change in hydroclimatic regime from nival to combined nival and pluvial. Average DOC flux over 30 years is predicted to decrease (24–27% of baseline) under elevated temperature scenario and increase (64–81% of baseline) under elevated temperature and precipitation scenario where a large increase in DOC export will occur in early winter. DOC flux in Baker Creek is controlled by runoff in the catchment. Under future climate scenario, increased DOC export from Baker Creek catchment with increased discharge can increase the mobility of previously deposited airborne metal contaminants such as arsenic from Giant Mine

Towards a National 3D Mapping Product for Great Britain

Knowing where something happens and where people are located can be critically important to understand issues ranging from climate change to road accidents, crime, schooling, transport and much more. To analyse these spatial problems, two-dimensional representations of the world, such as paper or digital maps, have traditionally been used. Geographic information systems (GIS) are the tools that enable capture, modelling, storage, retrieval, sharing, manipulation, analysis, and presentation of geographically referenced data. Three-dimensional geographic information (3D GI) is data that can represent real-world features as objects in 3D space. 3D GI offers additional functionality not possible in 2D, including analysing and querying volume, visibility, surface and sub-surface, and shadowing. This thesis contributes to the understanding of user requirements and other data related considerations in the production of 3D geographic information at a national level. The study promotes Ordnance Survey’s efforts in developing a 3D geographic product through: (1) identifying potential applications; (2) analysing existing 3D city modelling approaches; (3) eliciting and formalising user requirements; (4) developing metrics to describe the usefulness of 3D data and; (5) evaluating the commerciality of 3D GI. A review of current applications of 3D showed that visualisation dominated as the main use, allowing for better communication, and supporting decision-making processes. Reflecting this, an examination of existing 3D city models showed that, despite the varying modelling approaches, there was a general focus towards accurate and realistic geometric representation of the urban environment. Web-based questionnaires and semi-structured interviews revealed that while some applications (e.g. subsurface, photovoltaics, air and noise quality) lead the field with a high adoption of 3D, others were laggards due to organisational inertia (e.g. insurance, facilities management). Individuals expressed positive views on the use of 3D, but still struggled to justify the value and business case. Simple building geometry coupled with non-building thematic classes was perceived to be most useful by users. Several metrics were developed to quantify and compare the characteristics of thirty-three 3D datasets. Results showed that geometry-based metrics such as minimum feature length or Euler characteristic can be used to provide additional information as part of fitness-for-purpose evaluations. The metrics can also contribute to quality control during data production. An investigation into the commercial opportunities explored the economic value of 3D, the market size of 3D data in Great Britain, as well as proposed a number of opportunities within the wider business context of Ordnance Survey

Assessing the availability of remote sensing, hydrological modeling and in situ observations in snow cover research

Snow is an important component of the hydrological cycle. As a major part of the cryosphere, snow cover also represents a valuable terrestrial water resource. In the context of climate change, the dynamics of snow cover play a crucial role in rebalancing the global energy and water budgets. Remote sensing, hydrological modeling and in situ observations are three techniques frequently utilized for snowpack investigation. However, the uncertainties caused by systematic errors, scale issues, snow physics limit the availability of the three approaches in snow studies. This dissertation aims at the linkage of the three methods, seeking for a more effective way to understand the spatial-temporal behavior of seasonal snow cover at regional scales. Four case studies have been conducted in the Upper Rhine Region, southwestern Germany. A novel algorithm has been developed to improve the data quality of remotely sensed snow datasets with the help of ground-based meteorological observations. In particular, in situ snow depth measurements were involved into the cloud-gap-filling schemes of MODIS (Moderate Resolution Imaging Spectroradiometer) snow cover products with a conditional probability method. Meteorological filters generated by temperature, precipitation and snow depth data showed high performance in rejecting the overestimation errors of remotely sensed snow maps. A distributed hydrological model (TRAIN) was employed to simulate the seasonal snow cover, which was then validated against the improved cloud-free MODIS snow products and station-derived snow depth data, indicating a well model performance. The long-term trends of the simulated snow water equivalent as well as the recorded air temperature and precipitation were detected using Mann-Kendall trend test and Theil-Sen estimator, which showed a significant snow retreat at the high elevations and an intense warming trend in March during the study period of 1961-2008. Moreover, a snow monitoring network consisting of automatic weather stations, time-lapse photography and manual measurement was applied to reveal the complex snow processes in montane forest environments. Time-lapse photography proved great ability in collecting quantitative snow process information, such as snow canopy interception and blowing snow, suggesting a potential contribution to snow modeling. Finally, it was concluded that a synergistic application of remote sensing, hydrological modeling (with data assimilation) and field observations should be strengthened for the snow cover research in the future

Machine learning-based detection and mapping of riverine litter utilizing Sentinel-2 imagery

Despite the substantial impact of rivers on the global marine litter problem, riverine litter has been accorded inadequate consideration. Therefore, our objective was to detect riverine litter by utilizing middle-scale multispectral satellite images and machine learning (ML), with the Tisza River (Hungary) as a study area. The Very High Resolution (VHR) images obtained from the Google Earth database were employed to recognize some riverine litter spots (a blend of anthropogenic and natural substances). These litter spots served as the basis for training and validating five supervised machine-learning algorithms based on Sentinel-2 images [Artificial Neural Network (ANN), Support Vector Classifier (SVC), Random Forest (RF), Naïve Bays (NB) and Decision Tree (DT)]. To evaluate the generalization capability of the developed models, they were tested on larger unseen data under varying hydrological conditions and with different litter sizes. Besides the best-performing model was used to investigate the spatio-temporal variations of riverine litter in the Middel Tisza. According to the results, almost all the developed models showed favorable metrics based on the validation dataset (e.g., F1-score; SVC: 0.94, ANN: 0.93, RF: 0.91, DT: 0.90, and NB: 0.83); however, during the testing process, they showed medium (e.g., F1-score; RF:0.69, SVC: 0.62; ANN: 0.62) to poor performance (e.g., F1-score; NB: 0.48; DT: 0.45). The capability of all models to detect litter was bounded to the pixel size of the Sentinel-2 images. Based on the spatio-temporal investigation, hydraulic structures (e.g., Kisköre Dam) are the greatest litter accumulation spots. Although the highest transport rate of litter occurs during floods, the largest litter spot area upstream of the Kisköre Dam was observed at low stages in summer. This study represents a preliminary step in the automatic detection of riverine litter; therefore, additional research incorporating a larger dataset with more representative small litter spots, as well as finer spatial resolution images is necessary

GIS and Archaeology: Bison Hunting Strategies in Southern Saskatchewan

Between 1988 and 1989, an intensive archaeological survey of a small drainage known as Roan Mare coulee in southern Saskatchewan was conducted by Dr. Ernest Walker (Walker 1990). Among the 120 archaeological sites in the area, seven bison kills and a vast array of associated drivelines were identified. This study focuses upon the spatial interaction amongst the kills, the drivelines and the local environment in relation to the bison hunting strategies used on the Northern Plains. This is done by modelling where bison are likely to move in the terrain as well as how the topography obstructs their line of sight. As this problem covers a large spatial area and multiple different data sources, Geographic Information Systems (GIS) are integrated into the research design in the form of Least Cost Path and Viewshed analyses. Both archaeological data from Walker's survey and environmental data such as elevation and water sources served as the input datasets required by ArcGIS's spatial analysis tools. The results of the Least Cost Path analyses were compared visually to both the location and orientation of the driveline evidence, while the viewshed results were compared to the trap's location at the valley edge. The results of this research showed that the drivelines found at Roan Mare coulee appear to be following the general orientation of the landscape at the broadest scales, and likely served to funnel bison over large distances. There also appear to be several locations on the landscape that are amenable to moving bison to several different sites. The viewshed evidence shows the smaller scale nuances between bison vision and the terrain in a hypothetical drive event. The differences in the viewable area available to the bison at each site likely played a role in the chosen strategy employed when that site was used. It is hoped that this style of research can be continued with higher quality data and additional variables to help clarify many of the subtleties found in a Plains bison drive

Climatology of Rainfall Distribution and Asymmetries of Tropical Cyclones: A Global Perspective

Estimating the magnitude of tropical cyclone (TC) rainfall at different landfalling states is an important aspect of the TC forecast that directly affects the level of response from emergency managers in coastal areas. This research analyses the spatial distribution of the rainfall magnitude in tropical cyclones (TCs) at different stages over global oceans. The research’s central hypothesis is that TC rainfall exhibits distinct features in the long-term satellite dataset due to the evolution of the spatial distribution, radial variation, and asymmetries at the stages before, during, and after landfall. The resulting patterns are analyzed through a statistical approach that takes advantage of a 20-year global satellite database of rainfall retrievals from the TRMM/GPM constellation, with the aim to achieve two main objectives: 1) The first objective was to explore the global trends of TC rainfall rates using observational evidence provided by a satellite-based climatology. Results indicate there is an increasing trend in the global average TC rainfall rate of about 1.3% per year, with a more pronounced trend in the northern hemisphere than in the southern hemisphere. 2) The second objective was to examine the spatial distribution of the magnitude and axisymmetric intensity profiles of rainfall over the six TC-prone basins. The obtained differences were quantitatively investigated in terms of geographic location, sub-regions within the storm, and TC intensities. Results indicate that major hurricanes in the Atlantic basin exhibit heavier inner-core rainfall rates than those in any other basins, and this difference is highly correlated to specific environmental conditions. Overall, with the achievement of the above-described objectives, this document identifies and summarizes the dominant factors that control rainfall distribution in global TCs, mainly focused on the differences during landfilling processes

Groundwater Yield Modeling In The Fractured Bedrock Aquifers Of The Blue Ridge Physiographic Province, Watauga County, North Carolina

Groundwater is a commodity that is used by the majority of residents of Watauga County, North Carolina (NCDENR 2001; NC Cooperative Extension 2001). The fractured bedrock aquifers that house the water that these residents access by wells have long been known to be highly heterogeneous and thus problematic at times for locating ideal areas to drill productive wells. In 1967, the United States Geologic Survey (USGS) published a paper written by Harry LeGrand (LeGrand 1967) that predicatively modeled groundwater availability for the Blue Ridge and Piedmont Provinces in North Carolina. The model used various categories oftopographic position and regolith thickness to assess how productive (in gallons per minute) a potential site would be. The language used for describing LeGrand's (1967) topographic categories is familiar but vague. This study uses digital elevation model (DEM) derived surfaces to quantify and replicate LeGrand's (1967) topographic categories in the Geographic Information System (GIS) environment and to test the model against a database of wells in Watauga County